Computer Vision & Image AI

For transfer learning on YOLOv8, you typically need 300–1000 labeled images per class. For highly specialized use cases (medical imaging, rare defects), we use data augmentation and synthetic data generation to compensate for smaller datasets.

On well-labeled industrial datasets with good lighting, we consistently achieve 94–98% mAP@50. Medical and fine-grained applications may require more data and specialized architectures but can reach similar levels.

Yes. We optimize models with ONNX Runtime or TensorRT to run on edge hardware including Raspberry Pi, NVIDIA Jetson, industrial PLCs, and standard IP cameras with RTSP streams — no cloud required.

A focused single-class detection model (e.g., specific product defect) takes 2–3 weeks from data collection to deployed API. Multi-class systems with edge deployment and integration work typically take 6–8 weeks.

Yes. We provide end-to-end data labeling services using Roboflow or CVAT, including quality control review to ensure annotation consistency. For large datasets (10k+ images), we can coordinate a supervised human labeling team.

Absolutely. All model training is performed on isolated cloud instances or on-premise GPUs. Your images are never shared with third parties and are deleted from training infrastructure upon model delivery.